Object sorting work using an image includes, for example, appearance inspection of products in production lines of various products, removal of foods that are not able to be used in processing performed in factories in which fresh foods are processed, or the like. In sorting work of this type, based on training data including information for an image of an object (a normal article) which has no problem and information for an image of an object (an abnormal article) which has some kind of problem and is to be removed, a computer determines whether or not the objects depicted in the images are to be removed.
In generating training data, a computer (or a human) collects many captured images of an object that is a sorting target and gives a label used for determining, for each image, whether the image is an image obtained by capturing an object of a normal article or an abnormal article. In this case, as the number of images that are collected increases, sorting accuracy increases but, on the other hand, the number of times work of giving a label is performed increases and a work time becomes longer. Specifically, in a case in which a person (a worker) operates an input device to perform work of giving a label, as the number of images increases, a workload of the worker increases. Therefore, in recent years, a method for efficiently generating training data using information included in image data has been proposed.
As a method for generating training data, there is a method in which data that has been manually labeled by a person is prepared for each category and a category of data a label of which is unknown is determined to be a category of data the level of similarity of which is the highest among pieces of data which were labeled (see, for example, Japanese Laid-open Patent Publication No. 2001-158373).
Also, there is a method in which similar images are divided into clusters, based on a feature defined in advance, by learning without a teacher and it is determined, based on to which cluster an image that has been newly input belongs, whether the image is normal or abnormal (see, for example, Japanese Laid-open Patent Publication No. 2006-330797).
Japanese Laid-open Patent Publication No. 2001-158373 and Japanese Laid-open Patent Publication No. 2006-330797 discuss related art.